{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 查看当前挂载的数据集目录\n",
    "!ls /home/aistudio/data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 查看个人持久化工作区文件\n",
    "!ls /home/aistudio/work/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/\r\n",
      "Requirement already satisfied: torch in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (1.0.1.post2)\r\n",
      "Requirement already satisfied: torchvision in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (0.2.2.post3)\r\n",
      "Requirement already satisfied: six in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (from torchvision) (1.12.0)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (from torchvision) (1.15.4)\r\n",
      "Requirement already satisfied: pillow>=4.1.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.5/site-packages (from torchvision) (5.3.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install torch torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#导入包\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.models as models\n",
    "\n",
    "import copy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "style_img.size= torch.Size([1, 3, 128, 128])\n",
      "content_img.size= torch.Size([1, 3, 128, 128])\n"
     ]
    }
   ],
   "source": [
    "#度量\n",
    "use_cuda = torch.cuda.is_available()\n",
    "dtype = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor\n",
    "# desired size of the output image\n",
    "imsize = 512 if use_cuda else 128  # use small size if no gpu\n",
    "#图片导入和验证\n",
    "loader = transforms.Compose([\n",
    "    transforms.ToTensor()])  # transform it into a torch tensor\n",
    "\n",
    "\n",
    "def image_loader(image_name):\n",
    "    image = Image.open(image_name)\n",
    "    image= image.resize((imsize,imsize),Image.NEAREST)\n",
    "    image = Variable(loader(image))\n",
    "    # fake batch dimension required to fit network's input dimensions\n",
    "    image = image.unsqueeze(0)\n",
    "    return image\n",
    "\n",
    "#风格\n",
    "style_img = image_loader(\"style.jpg\").type(dtype)\n",
    "#原图\n",
    "content_img = image_loader(\"source.jpg\").type(dtype)\n",
    "print(\"style_img.size=\", style_img.size())\n",
    "print(\"content_img.size=\", content_img.size())\n",
    "assert style_img.size() == content_img.size(), \\\n",
    "    \"we need to import style and content images of the same size\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#图片展示\n",
    "unloader = transforms.ToPILImage()  # reconvert into PIL image\n",
    "\n",
    "plt.ion()\n",
    "\n",
    "def imshow(tensor, title=None):\n",
    "    image = tensor.clone().cpu()  # we clone the tensor to not do changes on it\n",
    "    image = image.view(3, imsize, imsize)  # remove the fake batch dimension\n",
    "    image = unloader(image)\n",
    "    plt.imshow(image)\n",
    "    if title is not None:\n",
    "        plt.title(title)\n",
    "    plt.pause(0.001) # pause a bit so that plots are updated\n",
    "\n",
    "\n",
    "plt.figure()\n",
    "imshow(style_img.data, title='Style Image')\n",
    "\n",
    "plt.figure()\n",
    "imshow(content_img.data, title='Content Image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class ContentLoss(nn.Module):\n",
    "\n",
    "    def __init__(self, target, weight):\n",
    "        super(ContentLoss, self).__init__()\n",
    "        # we 'detach' the target content from the tree used\n",
    "        self.target = target.detach() * weight\n",
    "        # to dynamically compute the gradient: this is a stated value,\n",
    "        # not a variable. Otherwise the forward method of the criterion\n",
    "        # will throw an error.\n",
    "        self.weight = weight\n",
    "        self.criterion = nn.MSELoss()\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.loss = self.criterion(input * self.weight, self.target)\n",
    "        self.output = input\n",
    "        return self.output\n",
    "\n",
    "    def backward(self, retain_graph=True):\n",
    "        self.loss.backward(retain_graph=retain_graph)\n",
    "        return self.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class GramMatrix(nn.Module):\n",
    "\n",
    "    def forward(self, input):\n",
    "        a, b, c, d = input.size()  # a=batch size(=1)\n",
    "        # b=number of feature maps\n",
    "        # (c,d)=dimensions of a f. map (N=c*d)\n",
    "\n",
    "        features = input.view(a * b, c * d)  # resise F_XL into \\hat F_XL\n",
    "\n",
    "        G = torch.mm(features, features.t())  # compute the gram product\n",
    "\n",
    "        # we 'normalize' the values of the gram matrix\n",
    "        # by dividing by the number of element in each feature maps.\n",
    "        return G.div(a * b * c * d)\n",
    "class StyleLoss(nn.Module):\n",
    "\n",
    "    def __init__(self, target, weight):\n",
    "        super(StyleLoss, self).__init__()\n",
    "        self.target = target.detach() * weight\n",
    "        self.weight = weight\n",
    "        self.gram = GramMatrix()\n",
    "        self.criterion = nn.MSELoss()\n",
    "\n",
    "    def forward(self, input):\n",
    "        self.output = input.clone()\n",
    "        self.G = self.gram(input)\n",
    "        self.G.mul_(self.weight)\n",
    "        self.loss = self.criterion(self.G, self.target)\n",
    "        return self.output\n",
    "\n",
    "    def backward(self, retain_graph=True):\n",
    "        self.loss.backward(retain_graph=retain_graph)\n",
    "        return self.loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cnn = models.vgg19(pretrained=True).features\n",
    "\n",
    "# move it to the GPU if possible:\n",
    "if use_cuda:\n",
    "    cnn = cnn.cuda()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# desired depth layers to compute style/content losses :\n",
    "content_layers_default = ['conv_4']\n",
    "style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']\n",
    "\n",
    "\n",
    "def get_style_model_and_losses(cnn, style_img, content_img,\n",
    "                               style_weight=1000, content_weight=1,\n",
    "                               content_layers=content_layers_default,\n",
    "                               style_layers=style_layers_default):\n",
    "    cnn = copy.deepcopy(cnn)\n",
    "\n",
    "    # just in order to have an iterable access to or list of content/syle\n",
    "    # losses\n",
    "    content_losses = []\n",
    "    style_losses = []\n",
    "\n",
    "    model = nn.Sequential()  # the new Sequential module network\n",
    "    gram = GramMatrix()  # we need a gram module in order to compute style targets\n",
    "\n",
    "    # move these modules to the GPU if possible:\n",
    "    if use_cuda:\n",
    "        model = model.cuda()\n",
    "        gram = gram.cuda()\n",
    "\n",
    "    i = 1\n",
    "    for layer in list(cnn):\n",
    "        if isinstance(layer, nn.Conv2d):\n",
    "            name = \"conv_\" + str(i)\n",
    "            model.add_module(name, layer)\n",
    "\n",
    "            if name in content_layers:\n",
    "                # add content loss:\n",
    "                target = model(content_img).clone()\n",
    "                content_loss = ContentLoss(target, content_weight)\n",
    "                model.add_module(\"content_loss_\" + str(i), content_loss)\n",
    "                content_losses.append(content_loss)\n",
    "\n",
    "            if name in style_layers:\n",
    "                # add style loss:\n",
    "                target_feature = model(style_img).clone()\n",
    "                target_feature_gram = gram(target_feature)\n",
    "                style_loss = StyleLoss(target_feature_gram, style_weight)\n",
    "                model.add_module(\"style_loss_\" + str(i), style_loss)\n",
    "                style_losses.append(style_loss)\n",
    "\n",
    "        if isinstance(layer, nn.ReLU):\n",
    "            name = \"relu_\" + str(i)\n",
    "            model.add_module(name, layer)\n",
    "\n",
    "            if name in content_layers:\n",
    "                # add content loss:\n",
    "                target = model(content_img).clone()\n",
    "                content_loss = ContentLoss(target, content_weight)\n",
    "                model.add_module(\"content_loss_\" + str(i), content_loss)\n",
    "                content_losses.append(content_loss)\n",
    "\n",
    "            if name in style_layers:\n",
    "                # add style loss:\n",
    "                target_feature = model(style_img).clone()\n",
    "                target_feature_gram = gram(target_feature)\n",
    "                style_loss = StyleLoss(target_feature_gram, style_weight)\n",
    "                model.add_module(\"style_loss_\" + str(i), style_loss)\n",
    "                style_losses.append(style_loss)\n",
    "\n",
    "            i += 1\n",
    "\n",
    "        if isinstance(layer, nn.MaxPool2d):\n",
    "            name = \"pool_\" + str(i)\n",
    "            model.add_module(name, layer)  # ***\n",
    "\n",
    "    return model, style_losses, content_losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#展示输入图片\n",
    "input_img = content_img.clone()\n",
    "# if you want to use a white noise instead uncomment the below line:\n",
    "# input_img = Variable(torch.randn(content_img.data.size())).type(dtype)\n",
    "\n",
    "# add the original input image to the figure:\n",
    "plt.figure()\n",
    "imshow(input_img.data, title='Input Image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def get_input_param_optimizer(input_img):\n",
    "    # this line to show that input is a parameter that requires a gradient\n",
    "    input_param = nn.Parameter(input_img.data)\n",
    "    optimizer = optim.LBFGS([input_param])\n",
    "    return input_param, optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def run_style_transfer(cnn, content_img, style_img, input_img, num_steps=300,\n",
    "                       style_weight=1000, content_weight=1):\n",
    "    \"\"\"Run the style transfer.\"\"\"\n",
    "    print('Building the style transfer model..')\n",
    "    model, style_losses, content_losses = get_style_model_and_losses(cnn,\n",
    "        style_img, content_img, style_weight, content_weight)\n",
    "    input_param, optimizer = get_input_param_optimizer(input_img)\n",
    "\n",
    "    print('Optimizing..')\n",
    "    run = [0]\n",
    "    while run[0] <= num_steps:\n",
    "\n",
    "        def closure():\n",
    "            # correct the values of updated input image\n",
    "            input_param.data.clamp_(0, 1)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            model(input_param)\n",
    "            style_score = 0\n",
    "            content_score = 0\n",
    "\n",
    "            for sl in style_losses:\n",
    "                style_score += sl.backward()\n",
    "            for cl in content_losses:\n",
    "                content_score += cl.backward()\n",
    "\n",
    "            run[0] += 1\n",
    "            if run[0] % 50 == 0:\n",
    "                print(\"run {}:\".format(run))\n",
    "                print('Style Loss : {:4f} Content Loss: {:4f}'.format(\n",
    "                    style_score.data.item(), content_score.data.item()))\n",
    "                print()\n",
    "                input_param.data.clamp_(0, 1)\n",
    "    \n",
    "                plt.figure()\n",
    "                imshow(input_param.data, title='Output Image')\n",
    "                # sphinx_gallery_thumbnail_number = 4\n",
    "                plt.ioff()\n",
    "                plt.show()\n",
    "\n",
    "            return style_score + content_score\n",
    "\n",
    "        optimizer.step(closure)\n",
    "        \n",
    "\n",
    "    # a last correction...\n",
    "    input_param.data.clamp_(0, 1)\n",
    "    \n",
    "    plt.figure()\n",
    "    imshow(input_param.data, title='Output Image')\n",
    "\n",
    "    # sphinx_gallery_thumbnail_number = 4\n",
    "    plt.ioff()\n",
    "    plt.show()\n",
    "\n",
    "    return input_param.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "output = run_style_transfer(cnn, content_img, style_img, input_img,num_steps=10000)\n",
    "\n",
    "plt.figure()\n",
    "imshow(output, title='Output Image')\n",
    "\n",
    "# sphinx_gallery_thumbnail_number = 4\n",
    "plt.ioff()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
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